1. Technical Field
The invention relates to a system for providing training samples for a pattern recognition device or classifier. Image processing systems often contain pattern recognition devices (classifiers).
2. Description of the Prior Art
Pattern recognition systems, loosely defined, are systems capable of distinguishing between various classes of real world stimuli according to their divergent characteristics. A number of applications require pattern recognition systems, which allow a system to deal with unrefined data without significant human intervention. By way of example, a pattern recognition system may attempt to classify individual letters to reduce a handwritten document to electronic text. Alternatively, the system may classify spoken utterances to allow verbal commands to be received at a computer console. In order to classify real-world stimuli, however, it is necessary to train the classifier to discriminate between classes by exposing it to a number of sample patterns.
Training a pattern recognition system requires a large number of samples to obtain acceptable accuracy rates. Often, the only difficulty in collecting these samples is one of expense, as examples of the items to be sorted are readily available. In some applications, however, samples will not be available in sufficient number for some or all of the output classes. By way of example, it is sometimes necessary to train the pattern recognition system prospectively to identify samples not yet commonly available. In such cases, it is impossible to obtain the necessary number of samples to properly train a classifier. Often, only a single prototypical sample will be available for each class. It would be desirable to generate a full set of training samples from this limited data.